Template "trait" class for OpenCV primitive data types. A primitive OpenCV data type is one of ``unsigned char``, ``bool``, ``signed char``, ``unsigned short``, ``signed short``, ``int``, ``float``, ``double``, or a tuple of values of one of these types, where all the values in the tuple have the same type. Any primitive type from the list can be defined by an identifier in the form ``CV_<bit-depth>{U|S|F}C(<number_of_channels>)``, for example: ``uchar`` ~ ``CV_8UC1``, 3-element floating-point tuple ~ ``CV_32FC3``, and so on. A universal OpenCV structure that is able to store a single instance of such a primitive data type is
:ocv:class:`Vec`. Multiple instances of such a type can be stored in a ``std::vector``, ``Mat``, ``Mat_``, ``SparseMat``, ``SparseMat_``, or any other container that is able to store ``Vec`` instances.
The ``DataType`` class is basically used to provide a description of such primitive data types without adding any fields or methods to the corresponding classes (and it is actually impossible to add anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not ``DataType`` itself that is used but its specialized versions, such as: ::
So, such traits are used to tell OpenCV which data type you are working with, even if such a type is not native to OpenCV. For example, the matrix ``B`` initialization above is compiled because OpenCV defines the proper specialized template class ``DataType<complex<_Tp> >`` . This mechanism is also useful (and used in OpenCV this way) for generic algorithms implementations.
An instance of the class is interchangeable with C structures, ``CvPoint`` and ``CvPoint2D32f`` . There is also a cast operator to convert point coordinates to the specified type. The conversion from floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion uses this
operation for each of the coordinates. Besides the class members listed in the declaration above, the following operations on points are implemented: ::
An instance of the class is interchangeable with the C structure ``CvPoint2D32f`` . Similarly to ``Point_`` , the coordinates of 3D points can be converted to another type. The vector arithmetic and comparison operations are also supported.
Template class for specifying the size of an image or rectangle. The class includes two members called ``width`` and ``height``. The structure can be converted to and from the old OpenCV structures
* Coordinates of the top-left corner. This is a default interpretation of ``Rect_::x`` and ``Rect_::y`` in OpenCV. Though, in your algorithms you may count ``x`` and ``y`` from the bottom-left corner.
OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the right and bottom boundaries are not. For example, the method ``Rect_::contains`` returns ``true`` if
The class represents rotated (i.e. not up-right) rectangles on a plane. Each rectangle is specified by the center point (mass center), length of each side (represented by cv::Size2f structure) and the rotation angle in degrees.
If you need a more flexible type, use :ocv:class:`Mat` . The elements of the matrix ``M`` are accessible using the ``M(i,j)`` notation. Most of the common matrix operations (see also
It is possible to convert ``Vec<T,2>`` to/from ``Point_``, ``Vec<T,3>`` to/from ``Point3_`` , and ``Vec<T,4>`` to :ocv:struct:`CvScalar` or :ocv:class:`Scalar_`. Use ``operator[]`` to access the elements of ``Vec``.
Being derived from ``Vec<_Tp, 4>`` , ``Scalar_`` and ``Scalar`` can be used just as typical 4-element vectors. In addition, they can be converted to/from ``CvScalar`` . The type ``Scalar`` is widely used in OpenCV to pass pixel values.
:ocv:class:`Mat` ) and for many other purposes. ``Range(a,b)`` is basically the same as ``a:b`` in Matlab or ``a..b`` in Python. As in Python, ``start`` is an inclusive left boundary of the range and ``end`` is an exclusive right boundary of the range. Such a half-opened interval is usually denoted as
The static method ``Range::all()`` returns a special variable that means "the whole sequence" or "the whole range", just like " ``:`` " in Matlab or " ``...`` " in Python. All the methods and functions in OpenCV that take ``Range`` support this special ``Range::all()`` value. But, of course, in case of your own custom processing, you will probably have to check and handle it explicitly: ::
The ``Ptr<_Tp>`` class is a template class that wraps pointers of the corresponding type. It is similar to ``shared_ptr`` that is part of the Boost library (
Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or a C structure. For some objects, like files, windows, mutexes, sockets, and others, a copy constructor or an assignment operator are difficult to define. For some other objects, like complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, some of complex OpenCV and your own data structures may be written in C. However, copy constructors and default constructors can simplify programming a lot. Besides, they are often required (for example, by STL containers). By wrapping a pointer to such a complex object ``TObj`` to ``Ptr<TObj>`` , you automatically get all of the necessary constructors and the assignment operator.
*O(1)* complexity of the above-mentioned operations. While some structures, like ``std::vector``, provide a copy constructor and an assignment operator, the operations may take a considerable amount of time if the data structures are large. But if the structures are put into ``Ptr<>`` , the overhead is small and independent of the data size.
Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers can store only objects of the same type and the same size. The classical solution to store objects of different types in the same container is to store pointers to the base class ``base_class_t*`` instead but then you loose the automatic memory management. Again, by using ``Ptr<base_class_t>()`` instead of the raw pointers, you can solve the problem.
The ``Ptr`` class treats the wrapped object as a black box. The reference counter is allocated and managed separately. The only thing the pointer class needs to know about the object is how to deallocate it. This knowledge is encapsulated in the ``Ptr::delete_obj()`` method that is called when the reference counter becomes 0. If the object is a C++ class instance, no additional coding is needed, because the default implementation of this method calls ``delete obj;`` .
However, if the object is deallocated in a different way, the specialized method should be created. For example, if you want to wrap ``FILE`` , the ``delete_obj`` may be implemented as follows: ::
..note:: The reference increment/decrement operations are implemented as atomic operations, and therefore it is normally safe to use the classes in multi-threaded applications. The same is true for :ocv:class:`Mat` and other C++ OpenCV classes that operate on the reference counters.
The class ``Mat`` represents an n-dimensional dense numerical single-channel or multi-channel array. It can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms may be better stored in a ``SparseMat`` ). The data layout of the array
Note that ``M.step[i] >= M.step[i+1]`` (in fact, ``M.step[i] >= M.step[i+1]*M.size[i+1]`` ). This means that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, and so on. ``M.step[M.dims-1]`` is minimal and always equal to the element size ``M.elemSize()`` .
So, the data layout in ``Mat`` is fully compatible with ``CvMat``, ``IplImage``, and ``CvMatND`` types from OpenCV 1.x. It is also compatible with the majority of dense array types from the standard toolkits and SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this compatibility, it is possible to make a ``Mat`` header for user-allocated data and process it in-place using OpenCV functions.
Use the ``create(nrows, ncols, type)`` method or the similar ``Mat(nrows, ncols, type[, fillValue])`` constructor. A new array of the specified size and type is allocated. ``type`` has the same meaning as in the ``cvCreateMat`` method.
For example, ``CV_8UC1`` means a 8-bit single-channel array, ``CV_32FC2`` means a 2-channel (complex) floating-point array, and so on.
As noted in the introduction to this chapter, ``create()`` allocates only a new array when the shape or type of the current array are different from the specified ones.
It passes the number of dimensions =1 to the ``Mat`` constructor but the created array will be 2-dimensional with the number of columns set to 1. So, ``Mat::dims`` is always >= 2 (can also be 0 when the array is empty).
Use a copy constructor or assignment operator where there can be an array or expression on the right side (see below). As noted in the introduction, the array assignment is an O(1) operation because it only copies the header and increases the reference counter. The ``Mat::clone()`` method can be used to get a full (deep) copy of the array when you need it.
Construct a header for a part of another array. It can be a single row, single column, several rows, several columns, rectangular region in the array (called a *minor* in algebra) or a diagonal. Such operations are also O(1) because the new header references the same data. You can actually modify a part of the array using this feature, for example:
Due to the additional ``datastart`` and ``dataend`` members, it is possible to compute a relative sub-array position in the main *container* array using ``locateROI()``:
Process "foreign" data using OpenCV (for example, when you implement a DirectShow* filter or a processing module for ``gstreamer``, and so on). For example:
Partial yet very common cases of this *user-allocated data* case are conversions from ``CvMat`` and ``IplImage`` to ``Mat``. For this purpose, there are special constructors taking pointers to ``CvMat`` or ``IplImage`` and the optional flag indicating whether to copy the data or not.
Backward conversion from ``Mat`` to ``CvMat`` or ``IplImage`` is provided via cast operators ``Mat::operator CvMat() const`` and ``Mat::operator IplImage()``. The operators do NOT copy the data.
With this approach, you first call a constructor of the :ocv:class:`Mat_` class with the proper parameters, and then you just put ``<<`` operator followed by comma-separated values that can be constants, variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation errors.
Once the array is created, it is automatically managed via a reference-counting mechanism. If the array header is built on top of user-allocated data, you should handle the data by yourself.
The array data is deallocated when no one points to it. If you want to release the data pointed by a array header before the array destructor is called, use ``Mat::release()`` .
The next important thing to learn about the array class is element access. This manual already described how to compute an address of each array element. Normally, you are not required to use the formula directly in the code. If you know the array element type (which can be retrieved using the method ``Mat::type()`` ), you can access the element
If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to the row first, and then just use the plain C operator ``[]`` : ::
Some operations, like the one above, do not actually depend on the array shape. They just process elements of an array one by one (or elements from multiple arrays that have the same coordinates, for example, array addition). Such operations are called *element-wise*. It makes sense to check whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If yes, process them as a long single row: ::
In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is smaller, which is especially noticeable in case of small matrices.
``A cmpop B, A cmpop alpha, alpha cmpop A``, where ``cmpop`` is one of ``: >, >=, ==, !=, <=, <``. The result of comparison is an 8-bit single channel mask whose elements are set to 255 (if the particular element or pair of elements satisfy the condition) or 0.
Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as ``norm``, ``mean``, ``sum``, ``countNonZero``, ``trace``, ``determinant``, ``repeat``, and others.
..note:: Comma-separated initializers and probably some other operations may require additional explicit ``Mat()`` or ``Mat_<T>()`` constructor calls to resolve a possible ambiguity.
:param type:Array type. Use ``CV_8UC1, ..., CV_64FC4`` to create 1-4 channel matrices, or ``CV_8UC(n), ..., CV_64FC(n)`` to create multi-channel (up to ``CV_MAX_CN`` channels) matrices.
:param s:An optional value to initialize each matrix element with. To set all the matrix elements to the particular value after the construction, use the assignment operator ``Mat::operator=(const Scalar& value)`` .
:param data:Pointer to the user data. Matrix constructors that take ``data`` and ``step`` parameters do not allocate matrix data. Instead, they just initialize the matrix header that points to the specified data, which means that no data is copied. This operation is very efficient and can be used to process external data using OpenCV functions. The external data is not automatically deallocated, so you should take care of it.
:param step:Number of bytes each matrix row occupies. The value should include the padding bytes at the end of each row, if any. If the parameter is missing (set to ``AUTO_STEP`` ), no padding is assumed and the actual step is calculated as ``cols*elemSize()`` . See :ocv:func:`Mat::elemSize` .
:param steps:Array of ``ndims-1`` steps in case of a multi-dimensional array (the last step is always set to the element size). If not specified, the matrix is assumed to be continuous.
:param m:Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied by these constructors. Instead, the header pointing to ``m`` data or its sub-array is constructed and associated with it. The reference counter, if any, is incremented. So, when you modify the matrix formed using such a constructor, you also modify the corresponding elements of ``m`` . If you want to have an independent copy of the sub-array, use ``Mat::clone()`` .
:param img:Pointer to the old-style ``IplImage`` image structure. By default, the data is shared between the original image and the new matrix. But when ``copyData`` is set, the full copy of the image data is created.
:param vec:STL vector whose elements form the matrix. The matrix has a single column and the number of rows equal to the number of vector elements. Type of the matrix matches the type of vector elements. The constructor can handle arbitrary types, for which there is a properly declared :ocv:class:`DataType` . This means that the vector elements must be primitive numbers or uni-type numerical tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is explicit. Since STL vectors are not automatically converted to ``Mat`` instances, you should write ``Mat(vec)`` explicitly. Unless you copy the data into the matrix ( ``copyData=true`` ), no new elements will be added to the vector because it can potentially yield vector data reallocation, and, thus, the matrix data pointer will be invalid.
:param copyData:Flag to specify whether the underlying data of the STL vector or the old-style ``CvMat`` or ``IplImage`` should be copied to (``true``) or shared with (``false``) the newly constructed matrix. When the data is copied, the allocated buffer is managed using ``Mat`` reference counting mechanism. While the data is shared, the reference counter is NULL, and you should not deallocate the data until the matrix is not destructed.
:param rowRange:Range of the ``m`` rows to take. As usual, the range start is inclusive and the range end is exclusive. Use ``Range::all()`` to take all the rows.
These are various constructors that form a matrix. As noted in the :ref:`AutomaticAllocation`,
often the default constructor is enough, and the proper matrix will be allocated by an OpenCV function. The constructed matrix can further be assigned to another matrix or matrix expression or can be allocated with
:param m:Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that no data is copied but the data is shared and the reference counter, if any, is incremented. Before assigning new data, the old data is de-referenced via :ocv:func:`Mat::release` .
:param expr:Assigned matrix expression object. As opposite to the first form of the assignment operation, the second form can reuse already allocated matrix if it has the right size and type to fit the matrix expression result. It is automatically handled by the real function that the matrix expressions is expanded to. For example, ``C=A+B`` is expanded to ``add(A, B, C)`` , and :func:`add` takes care of automatic ``C`` reallocation.
The method makes a new header for the specified matrix row and returns it. This is an O(1) operation, regardless of the matrix size. The underlying data of the new matrix is shared with the original matrix. Here is the example of one of the classical basic matrix processing operations, ``axpy``, used by LU and many other algorithms: ::
This happens because ``A.row(i)`` forms a temporary header that is further assigned to another header. Remember that each of these operations is O(1), that is, no data is copied. Thus, the above assignment is not true if you may have expected the j-th row to be copied to the i-th row. To achieve that, you should either turn this simple assignment into an expression or use the :ocv:func:`Mat::copyTo` method: ::
The method makes a new header for the specified matrix column and returns it. This is an O(1) operation, regardless of the matrix size. The underlying data of the new matrix is shared with the original matrix. See also the
The method creates a full copy of the array. The original ``step[]`` is not taken into account. So, the array copy is a continuous array occupying ``total()*elemSize()`` bytes.
so that the destination matrix is reallocated if needed. While ``m.copyTo(m);`` works flawlessly, the function does not handle the case of a partial overlap between the source and the destination matrices.
When the operation mask is specified, and the ``Mat::create`` call shown above reallocated the matrix, the newly allocated matrix is initialized with all zeros before copying the data.
:param rtype:Desired destination matrix type or, rather, the depth since the number of channels are the same as the source has. If ``rtype`` is negative, the destination matrix will have the same type as the source.
The method makes a new matrix header for ``*this`` elements. The new matrix may have a different size and/or different number of channels. Any combination is possible if:
No extra elements are included into the new matrix and no elements are excluded. Consequently, the product ``rows*cols*channels()`` must stay the same after the transformation.
No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of rows, or the operation changes the indices of elements row in some other way, the matrix must be continuous. See
The method performs matrix transposition by means of matrix expressions. It does not perform the actual transposition but returns a temporary matrix transposition object that can be further used as a part of more complex matrix expressions or can be assigned to a matrix: ::
***DECOMP_CHOLESKY** is the Cholesky :math:`LL^T` decomposition for symmetrical positively defined matrices only. This type is about twice faster than LU on big matrices.
The method performs a matrix inversion by means of matrix expressions. This means that a temporary matrix inversion object is returned by the method and can be used further as a part of more complex matrix expressions or can be assigned to a matrix.
The method returns a temporary object encoding per-element array multiplication, with optional scale. Note that this is not a matrix multiplication that corresponds to a simpler "*" operator.
The method computes a cross-product of two 3-element vectors. The vectors must be 3-element floating-point vectors of the same shape and size. The result is another 3-element vector of the same shape and type as operands.
The method computes a dot-product of two matrices. If the matrices are not single-column or single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D vectors. The vectors must have the same size and type. If the matrices have more than one channel, the dot products from all the channels are summed together.
The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant array as a function parameter, part of a matrix expression, or as a matrix initializer. ::
In the example above, a new matrix is allocated only if ``A`` is not a 3x3 floating-point matrix. Otherwise, the existing matrix ``A`` is filled with zeros.
The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it just remembers the scale factor (3 in this case) and use it when actually invoking the matrix initializer.
This is one of the key ``Mat`` methods. Most new-style OpenCV functions and methods that produce arrays call this method for each output array. The method uses the following algorithm:
Such a scheme makes the memory management robust and efficient at the same time and helps avoid extra typing for you. This means that usually there is no need to explicitly allocate output arrays. That is, instead of writing: ::
:ocv:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It is called implicitly by the matrix assignment operator. The reference counter increment is an atomic operation on the platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in different threads.
The method decrements the reference counter associated with the matrix data. When the reference counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers are set to NULL's. If the matrix header points to an external data set (see
This method can be called manually to force the matrix data deallocation. But since this method is automatically called in the destructor, or by any other method that changes the data pointer, it is usually not needed. The reference counter decrement and check for 0 is an atomic operation on the platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in different threads.
The methods change the number of matrix rows. If the matrix is reallocated, the first ``min(Mat::rows, sz)`` rows are preserved. The methods emulate the corresponding methods of the STL vector class.
The method reserves space for ``sz`` rows. If the matrix already has enough space to store ``sz`` rows, nothing happens. If the matrix is reallocated, the first ``Mat::rows`` rows are preserved. The method emulates the corresponding method of the STL vector class.
The methods add one or more elements to the bottom of the matrix. They emulate the corresponding method of the STL vector class. When ``elem`` is ``Mat`` , its type and the number of columns must be the same as in the container matrix.
:ocv:func:`Mat::colRange` , and others, the resultant submatrix points just to the part of the original big matrix. However, each submatrix contains information (represented by ``datastart`` and ``dataend`` fields) that helps reconstruct the original matrix size and the position of the extracted submatrix within the original matrix. The method ``locateROI`` does exactly that.
:ocv:func:`Mat::locateROI` . The typical use of these functions is to determine the submatrix position within the parent matrix and then shift the position somehow. Typically, it can be required for filtering operations when pixels outside of the ROI should be taken into account. When all the method parameters are positive, the ROI needs to grow in all directions by the specified amount, for example:::
In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the filtering with the 5x5 kernel.
``adjustROI`` forces the adjusted ROI to be inside of the parent matrix that is boundaries of the adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix ``A`` is located in the first row of a parent matrix and you called ``A.adjustROI(2, 2, 2, 2)`` then ``A`` will not be increased in the upward direction.
:ocv:func:`Mat::colRange` . For example, ``A(Range(0, 10), Range::all())`` is equivalent to ``A.rowRange(0, 10)`` . Similarly to all of the above, the operators are O(1) operations, that is, no matrix data is copied.
The operator creates the ``CvMat`` header for the matrix without copying the underlying data. The reference counter is not taken into account by this operation. Thus, you should make sure than the original matrix is not deallocated while the ``CvMat`` header is used. The operator is useful for intermixing the new and the old OpenCV API's, for example: ::
The operator creates the ``IplImage`` header for the matrix without copying the underlying data. You should make sure than the original matrix is not deallocated while the ``IplImage`` header is used. Similarly to ``Mat::operator CvMat`` , the operator is useful for intermixing the new and the old OpenCV API's.
The method returns ``true`` if the matrix elements are stored continuously without gaps at the end of each row. Otherwise, it returns ``false``. Obviously, ``1x1`` or ``1xN`` matrices are always continuous. Matrices created with
The continuity flag is stored as a bit in the ``Mat::flags`` field and is computed automatically when you construct a matrix header. Thus, the continuity check is a very fast operation, though theoretically it could be done as follows: ::
The method is used in quite a few of OpenCV functions. The point is that element-wise operations (such as arithmetic and logical operations, math functions, alpha blending, color space transformations, and others) do not depend on the image geometry. Thus, if all the input and output arrays are continuous, the functions can process them as very long single-row vectors. The example below illustrates how an alpha-blending function can be implemented. ::
This approach, while being very simple, can boost the performance of a simple element-operation by 10-20 percents, especially if the image is rather small and the operation is quite simple.
:ocv:func:`Mat::create` for the destination array, that allocates the destination array unless it already has the proper size and type. And while the newly allocated arrays are always continuous, you still need to check the destination array because :ocv:func:`Mat::create` does not always allocate a new matrix.
The method returns the matrix element channel size in bytes, that is, it ignores the number of channels. For example, if the matrix type is ``CV_16SC3`` , the method returns ``sizeof(short)`` or 2.
The method returns a matrix element type. This is an identifier compatible with the ``CvMat`` type system, like ``CV_16SC3`` or 16-bit signed 3-channel array, and so on.
The method returns the identifier of the matrix element depth (the type of each individual channel). For example, for a 16-bit signed 3-channel array, the method returns ``CV_16S`` . A complete list of matrix types contains the following values:
The method returns ``true`` if ``Mat::total()`` is 0 or if ``Mat::data`` is NULL. Because of ``pop_back()`` and ``resize()`` methods ``M.total() == 0`` does not imply that ``M.data == NULL`` .
The template methods return a reference to the specified array element. For the sake of higher performance, the index range checks are only performed in the Debug configuration.
Note that the variants with a single index (i) can be used to access elements of single-row or single-column 2-dimensional arrays. That is, if, for example, ``A`` is a ``1 x N`` floating-point matrix and ``B`` is an ``M x 1`` integer matrix, you can simply write ``A.at<float>(k+4)`` and ``B.at<int>(2*i+1)`` instead of ``A.at<float>(0,k+4)`` and ``B.at<int>(2*i+1,0)`` , respectively.
The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very similar to the use of bi-directional STL iterators. In the example below, the alpha blending function is rewritten using the matrix iterators: ::
The class ``Mat_<_Tp>`` is a "thin" template wrapper on top of the ``Mat`` class. It does not have any extra data fields. Nor this class nor ``Mat`` has any virtual methods. Thus, references or pointers to these two classes can be freely but carefully converted one to another. For example: ::
While ``Mat`` is sufficient in most cases, ``Mat_`` can be more convenient if you use a lot of element access operations and if you know matrix type at the compilation time. Note that ``Mat::at<_Tp>(int y, int x)`` and ``Mat_<_Tp>::operator ()(int y, int x)`` do absolutely the same and run at the same speed, but the latter is certainly shorter: ::
where ``_InputArray`` is a class that can be constructed from ``Mat``, ``Mat_<T>``, ``Matx<T, m, n>``, ``std::vector<T>``, ``std::vector<std::vector<T> >`` or ``std::vector<Mat>``. It can also be constructed from a matrix expression.
Since this is mostly implementation-level class, and its interface may change in future versions, we do not describe it in details. There are a few key things, though, that should be kept in mind:
* When you see in the reference manual or in OpenCV source code a function that takes ``InputArray``, it means that you can actually pass ``Mat``, ``Matx``, ``vector<T>`` etc. (see above the complete list).
* Optional input arguments: If some of the input arrays may be empty, pass ``cv::noArray()`` (or simply ``cv::Mat()`` as you probably did before).
* The class is designed solely for passing parameters. That is, normally you *should not* declare class members, local and global variables of this type.
* If you want to design your own function or a class method that can operate of arrays of multiple types, you can use ``InputArray`` (or ``OutputArray``) for the respective parameters. Inside a function you should use ``_InputArray::getMat()`` method to construct a matrix header for the array (without copying data). ``_InputArray::kind()`` can be used to distinguish ``Mat`` from ``vector<>`` etc., but normally it is not needed.
That is, we form an STL vector containing points, and apply in-place affine transformation to the vector using the 2x3 matrix created inline as ``Matx<float, 2, 3>`` instance.
Here is how such a function can be implemented (for simplicity, we implement a very specific case of it, according to the assertion statement inside) ::
It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation level their use is similar, but ``_InputArray::getMat(idx)`` should be used to get header for the idx-th component of the outer vector and ``_InputArray::size().area()`` should be used to find the number of components (vectors/matrices) of the outer vector.
This type is very similar to ``InputArray`` except that it is used for input/output and output function parameters. Just like with ``InputArray``, OpenCV users should not care about ``OutputArray``, they just pass ``Mat``, ``vector<T>`` etc. to the functions. The same limitation as for ``InputArray``: **Do not explicitly create OutputArray instances** applies here too.
If you want to make your function polymorphic (i.e. accept different arrays as output parameters), it is also not very difficult. Take the sample above as the reference. Note that ``_OutputArray::create()`` needs to be called before ``_OutputArray::getMat()``. This way you guarantee that the output array is properly allocated.
Optional output parameters. If you do not need certain output array to be computed and returned to you, pass ``cv::noArray()``, just like you would in the case of optional input array. At the implementation level, use ``_OutputArray::needed()`` to check if certain output array needs to be computed or not.
There are several synonyms for ``OutputArray`` that are used to assist automatic Python/Java/... wrapper generators: ::
Use the class to implement unary, binary, and, generally, n-ary element-wise operations on multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some may be not. It is possible to use conventional
``MatIterator`` 's for each array but incrementing all of the iterators after each small operations may be a big overhead. In this case consider using ``NAryMatIterator`` to iterate through several matrices simultaneously as long as they have the same geometry (dimensionality and all the dimension sizes are the same). On each iteration ``it.planes[0]``, ``it.planes[1]`` , ... will be the slices of the corresponding matrices.
:ocv:class:`Mat` can store. *Sparse* means that only non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its stored elements can actually become 0. It is up to you to detect such elements and delete them using ``SparseMat::erase`` ). The non-zero elements are stored in a hash table that grows when it is filled so that the search time is O(1) in average (regardless of whether element is there or not). Elements can be accessed using the following methods:
Sparse matrix iterators. They are similar to ``MatIterator`` but different from :ocv:class:`NAryMatIterator`. That is, the iteration loop is familiar to STL users:
If you run this loop, you will notice that elements are not enumerated in a logical order (lexicographical, and so on). They come in the same order as they are stored in the hash table (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. Note, however, that pointers to the nodes may become invalid when you add more elements to the matrix. This may happen due to possible buffer reallocation.
Combination of the above 2 methods when you need to process 2 or more sparse matrices simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 floating-point sparse matrices:
This is a base class for all more or less complex algorithms in OpenCV, especially for classes of algorithms, for which there can be multiple implementations. The examples are stereo correspondence (for which there are algorithms like block matching, semi-global block matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck etc.).
* so called "virtual constructor". That is, each Algorithm derivative is registered at program start and you can get the list of registered algorithms and create instance of a particular algorithm by its name (see ``Algorithm::create``). If you plan to add your own algorithms, it is good practice to add a unique prefix to your algorithms to distinguish them from other algorithms.
* setting/retrieving algorithm parameters by name. If you used video capturing functionality from OpenCV highgui module, you are probably familar with ``cvSetCaptureProperty()``, ``cvGetCaptureProperty()``, ``VideoCapture::set()`` and ``VideoCapture::get()``. ``Algorithm`` provides similar method where instead of integer id's you specify the parameter names as text strings. See ``Algorithm::set`` and ``Algorithm::get`` for details.
* reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store all its parameters and then read them back. There is no need to re-implement it each time.
The method returns value of the particular parameter. Since the compiler can not deduce the type of the returned parameter, you should specify it explicitly in angle brackets. Here are the allowed forms of get:
In some cases the actual type of the parameter can be cast to the specified type, e.g. integer parameter can be cast to double, ``bool`` can be cast to ``int``. But "dangerous" transformations (string<->number, double->int, 1x1 Mat<->number, ...) are not performed and the method will throw an exception. In the case of ``Mat`` or ``vector<Mat>`` parameters the method does not clone the matrix data, so do not modify the matrices. Use ``Algorithm::set`` instead - slower, but more safe.
The method sets value of the particular parameter. Some of the algorithm parameters may be declared as read-only. If you try to set such a parameter, you will get exception with the corresponding error message.
The method stores all the algorithm parameters (in alphabetic order) to the file storage. The method is virtual. If you define your own Algorithm derivative, your can override the method and store some extra information. However, it's rarely needed. Here are some examples:
* SIFT feature detector (from nonfree module). The class only stores algorithm parameters and no keypoints or their descriptors. Therefore, it's enough to store the algorithm parameters, which is what ``Algorithm::write()`` does. Therefore, there is no dedicated ``SIFT::write()``.
* Background subtractor (from video module). It has the algorithm parameters and also it has the current background model. However, the background model is not stored. First, it's rather big. Then, if you have stored the background model, it would likely become irrelevant on the next run (because of shifted camera, changed background, different lighting etc.). Therefore, ``BackgroundSubtractorMOG`` and ``BackgroundSubtractorMOG2`` also rely on the standard ``Algorithm::write()`` to store just the algorithm parameters.
* Expectation Maximization (from ml module). The algorithm finds mixture of gaussians that approximates user data best of all. In this case the model may be re-used on the next run to test new data against the trained statistical model. So EM needs to store the model. However, since the model is described by a few parameters that are available as read-only algorithm parameters (i.e. they are available via ``EM::get()``), EM also relies on ``Algorithm::write()`` to store both EM parameters and the model (represented by read-only algorithm parameters).
The method reads all the algorithm parameters from the specified node of a file storage. Similarly to ``Algorithm::write()``, if you implement an algorithm that needs to read some extra data and/or re-compute some internal data, you may override the method.
This static method creates a new instance of the specified algorithm. If there is no such algorithm, the method will silently return null pointer (that can be checked by ``Ptr::empty()`` method). Also, you should specify the particular ``Algorithm`` subclass as ``_Tp`` (or simply ``Algorithm`` if you do not know it at that point). ::
..note:: This is important note about seemingly mysterious behavior of ``Algorithm::create()`` when it returns NULL while it should not. The reason is simple - ``Algorithm::create()`` resides in OpenCV`s core module and the algorithms are implemented in other modules. If you create algorithms dynamically, C++ linker may decide to throw away the modules where the actual algorithms are implemented, since you do not call any functions from the modules. To avoid this problem, you need to call ``initModule_<modulename>();`` somewhere in the beginning of the program before ``Algorithm::create()``. For example, call ``initModule_nonfree()`` in order to use SURF/SIFT, call ``initModule_ml()`` to use expectation maximization etc.
The above methods are usually enough for users. If you want to make your own algorithm, derived from ``Algorithm``, you should basically follow a few conventions and add a little semi-standard piece of code to your class:
* Make a class and specify ``Algorithm`` as its base class.
* The algorithm parameters should be the class members. See ``Algorithm::get()`` for the list of possible types of the parameters.
* Add public virtual method ``AlgorithmInfo* info() const;`` to your class.
* Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take http://code.opencv.org/svn/opencv/trunk/opencv/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
* Add some public function (e.g. ``initModule_<mymodule>()``) that calls info() of your algorithm and put it into the same source file as ``info()`` implementation. This is to force C++ linker to include this object file into the target application. See ``Algorithm::create()`` for details.
